Shoag-Veuger-Ban-the-box-WP-updated-June

Our central finding is that these policies raise the employment of residents of the top quartile of high-crime neighborhoods by as much as 4%; these are also the neighborhoods with the g

“Ban the box” measures help high crime

neighborhoods

Daniel Shoag

Harvard Kennedy School and Case Western Reserve University

Stan Veuger

American Enterprise Institute

AEI Economics Working Paper 2016-08

“Ban the Box” Measures Help High-Crime Neighborhoods i

in high-crime neighborhoods by up to 4%, consistent with the central objective of these

measures This effect can be seen in both aggregate employment patterns for high-crime

neighborhoods and in commuting patterns to workplace destinations with this type of ban The increases are particularly large in the public sector and in lower-wage jobs This is the first nationwide evidence that these policies do, indeed, increase employment opportunities in

neighborhoods with many ex-offenders

i We thank Nikolai Boboshko, Philip Hoxie, and Hao-Kai Pai for excellent research assistance Dennis Carlton, Jeffrey Clemens, Terry-Ann Craigie, Jennifer Doleac, Carolina Ferrerosa-Young, Harry Holzer, Michael LeFors, Magne Mogstad, Michael Strain, Rebecca Thorpe, Xintong Wang, and an anonymous referee, as well as attendees at the Annual Conferences of the American Economic Association, the American Political Science Association, the

Midwest Economic Association, the Midwest Political Science Association, and the Southern Economic Association, the Bureau of Economic Analysis, the Fall Research Conference of the Association for Public Policy Analysis and Management, the Harvard Kennedy School, and the U.S Census Bureau, Local Employment Dynamics Partnership, and Council for Community and Economic Research Webinar provided insightful comments and helpful

suggestions We are particularly grateful to the late Devah Pager for her guidance

ii Case Western Reserve University Weatherhead School of Management and Harvard Kennedy School,

dxs788@case.edu

iii American Enterprise Institute for Public Policy Research, IE School of Global and Public Affairs, and Tilburg University Corresponding Author: American Enterprise Institute, 1789 Massachusetts Avenue, Washington, DC

20036, stan.veuger@aei.org

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Slightly fewer than half of all private-sector firms and practically all government agencies in the United States include questions along the lines of “Have you ever been convicted of a crime?” in employment applications, or ask applicants to check a box to indicate that they have been

convicted of a crime (Connerley et al., 2001) Efforts to remove such questions have gained steam over the past couple of decades as increasingly large numbers of Americans saw their chances of gainful employment limited by the interplay of mass incarceration and employers’ reluctance to hire convicts (Pager et al., 2009; The Sentencing Project, 2019) In response, various jurisdictions, government agencies, and private-sector firms decided to eliminate

questions about applicants’ criminal background on application documents or to mandate that employers do so, i.e., to “ban the box” (Avery, 2019; Stacey and Cohen, 2017)

Our goal in this paper is to study the effects of this latter response - bans on questions about criminal records (early on) in employee screening processes - on workers in high-crime

neighborhoods Our central finding is that these policies raise the employment of residents of the top quartile of high-crime neighborhoods by as much as 4%; these are also the neighborhoods with the greatest population of workers with criminal records This robust increase is in large part driven by residents getting hired into the public sector, where compliance is likely to be highest and which is often the central target of these bans The greatest increases occur in the lowest-wage jobs What this shows is that, perhaps surprisingly, Ban the Box measures can be seen as effective place-based policies

The recency of Ban the Box measures means that research on their consequences has so far been limited In addition, previous work, e.g Doleac and Hansen (forthcoming), focused mainly on the distributional consequences of these policies along racial and age lines, in particular changes

in outcomes for young black men, in order to identify potential unintended consequences of the

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bans We focus instead on evaluating these policies by studying their impact on the labor market performance of workers with criminal records, the group specifically targeted, to see whether the

policies’ intended consequences materialized We also use hyperlocal (census tract level) data

that allow us to identify the beneficiaries of Ban the Box policies at a more granular level than the MSA-level changes studied by Doleac and Hansen

The paper most directly related to our work is by Jackson and Zhao (2016), who study the

introduction of Ban the Box in Massachusetts in late 2009 They link ex-offenders’ criminal records to unemployment insurance quarterly wage records, and find that their employment does not vary much in the year after Ban the Box was introduced Jackson and Zhao construct a control group of workers without criminal records, but can only match them to treated workers based on age and residential location, not on skill or educational attainment This makes it

difficult to adequately control for potentially differential trends stemming from the financial crisis that occurred at the same time

We do not use individual-level criminal records Instead, our contributions are that we provide nationwide estimates of the impact of Ban the Box rules on high-crime neighborhoods, which is where workers with criminal records are likely to reside; we present a broader range of

identification strategies; and we are not restricted to a Ban the Box measure implemented at the very nadir of the Great Recession’s labor market experience We exploit variation in whether and when a range of cities, counties, and states implemented them to identify their significance using LEHD Origin-Destination Employment Statistics (LODES) on employment outcomes We do this, mostly, with difference-in-difference, triple-difference, and quadruple-difference estimators that compare different groups and small neighborhoods within cities as these cities adopt bans at different points in time For example, one specification compares residents of a census tract who

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work in a tract that became subject to Ban the Box rules to residents of the same tract who work

in a tract that did not become subject to such rules, before and after implementation

We proceed as follows In the next section, we present background information on the role played by employee screening procedures and criminal records in hiring processes, the roll-out

of the policies we study, and the conceptual framework within which we will evaluate their effectiveness In Section II we introduce the data we will draw upon in that evaluation Section III explains why we focus on high-crime neighborhoods: their residents are more likely to have criminal records We then discuss the impact of Ban the Box measures on employment in such neighborhoods (section IV), and the industries and income categories in which these

employment effects materialize (section V) Section VI concludes by discussing the implications

of our findings for public policy

In the early stages of interacting with potential employers, job seekers are often asked whether they have ever been convicted of a crime In addition, many organizations run criminal

background checks on potential employees, forcing applicants to respond truthfully For

example, roughly 17% of the job listings in the large database of postings collected by Burning Glass Technologies, a leading provider of online job market data, announce such checks in the advertisement itself This represents a lower bound: estimates of the share of organizations carrying them out range from slightly fewer than half of all private-sector firms to practically all government agencies (Connerley et al., 2001) Oft-cited goals of these employee screening practices are to mitigate risk of fraud or criminal activity by employees (Hughes et al., 2013), to protect oneself from negligent hiring lawsuits (Connerley et al., 2001), or, more generally, to

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avoid employing persons of poor character, skills, and work ethic, or who are likely to be

arrested again soon (Freeman, 2008; Gerlach, 2006) In addition, federal and state laws ban certain employers, including public-sector employers, from hiring ex-offenders for certain

positions and/or mandate criminal background checks (Freeman, 2008)

Job applicants are thus likely to be confronted with inquiries regarding any past run-ins with the law, and they are also likely to be excluded from consideration or subjected to additional scrutiny

by potential employers if they have experienced any (Stoll and Bushway, 2008) This affects a significant chunk of the population: as many as 65 million people are estimated to have been arrested and/or convicted of criminal offenses (Natividad Rodriguez and Emsellem, 2011) Different groups are affected to dramatically different extents Whereas about one out of every three African-American males, and one out of six Hispanic males will spend time incarcerated over their lifetime (Bonczar, 2003), women are convicted at much lower rates, and account for only 7% of the federal and state prison population (Carson, 2015)

This state of affairs has long concerned some academics, activists, and policymakers, because making it harder for convicts to find gainful employment may increase rates of recidivism while reducing the output and productivity of these potential workers (Henry and Jacobs, 2007;

Nadich, 2014; The White House, 2015; Council of Economic Advisers, 2016) In addition, the adoption of an applicant’s criminal history as a key hiring criterion is presumed to have an adverse impact on minority applicants because African Americans and Hispanics represent a much larger share of arrestees and convicts than their population share (Henry, 2008)

To assuage such concerns, a sizable numbers of cities, counties, and states have adopted

legislation or other measures that prohibit the use of criminal background questions in the early

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stages of application procedures, starting with the state of Hawaii in 1998 As Figure 1 and Appendix Table 1a and 1b show, in the last five years we have witnessed a veritable explosion of activity on this front In 2015, the federal government followed suit via executive order (Korte, 2015) This was followed by the Fair Chance Act, included in the 2020 National Defense

Authorization Act, which restricted the use of criminal background questions by federal

contractors as well as the federal government itself (see Craigie et al., 2019) Additionally, a number of private-sector employers, most prominently Home Depot, Koch Industries, Target, and Walmart, have also recently adopted a policy of not asking job applicants about their

criminal history (Levine, 2015; Staples, 2013)

These policies reflect a conceptualization of the way in which employers approach the decision

of whether to hire an applicant as a screening problem, similar to those in Aigner and Cain (1977), Autor and Scarborough (2008), or Wozniak (2015) Employers want to hire high-

productivity workers, and try to assess the productivity of job applicants They cannot

necessarily rely on applicants’ self-identification, as applicants have an incentive to present themselves as high-productivity even when they are low-productivity workers Instead,

employers rely on signals they receive about worker quality One commonly used signal is the applicant’s criminal history, which is taken to proxy for low productivity If employers rely on this signal in the screening process, it makes it more difficult for applicants with criminal records

to find suitable employment If they do not, applicants with criminal records will find it easier to find work Finally, if employers delay reliance on the criminal-records signal until later in the application process, as they (are forced to) do under Ban the Box policies, the signals collected earlier in the application process may reduce the weight placed on applicants’ criminal record, which will also help such applicants

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A possible concern is that under a ban on the (early) use of a specific signal, employers will start relying (more) on other signals to proxy for productivity Such signals may include education and experience (as in Clifford and Shoag, 2016) or race (as studied by Holzer et al (2006), Agan and Starr’s (2018), Craigie (2020), and Doleac and Hansen (forthcoming)), and may themselves negatively affect the employment prospects of other or overlapping marginalized groups of workers We address this concern in more detail in Section VI Even so, with Ban the Box

measures in place, we would expect more applicants with criminal records to be hired Such applicants are likely to live in high-crime neighborhoods, as we will see, and we should thus expect employment in such neighborhoods to increase Let us turn now to the data we will use to test this prediction empirically

National Employment Law Project

The National Employment Law Project, as a part of its “Fair Chance” campaign, collects and disseminates data on city-, county- and state-level Ban the Box policies Summaries of the bills and executive orders restricting or eliminating inquiries into applicants’ criminal background that have been adopted at different levels of government are readily available in its guide on state and local policies and on its website (Natividad Rodriguez and Avery, 2016) Although these policies vary in their restrictiveness and in how comprehensively they apply to employers and producers, for the purpose of our analysis we do not draw such distinctions, partially to avoid arbitrary assignments of treatment regimes, and partially because we believe that sector-specific or public-sector-only measures may well have spillover effects on other sectors Such spillovers can arise

in a variety of ways For example, sector-specific Ban the Box measures may create a new social

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norm that guides employers throughout the economy In addition, Ban the Box measures may produce spillover effects in general equilibrium, as workers without criminal records may be displaced from directly affected sectors but find employment in other industries The latter effect resembles the general-equilibrium spillovers from trade shocks in Monte (2016) Appendix Tables 1a and 1b provide a list of state and local government entities that had passed Ban the Box measures by the end of 2013 and when they did so, while Figure 1 shows the cities in our sample, to be discussed below, that had passed such measures by then

on an identification approach that exploits variation in crime rates between census tracts, we limit those parts of our analysis to these cities We rank census tracts based on the number of assaults and murders per capita, and label the 25% most violent tracts as “high-crime.” Figure 2 shows that the crime rate distribution of tracts displays significant skewness While any specific number is arbitrary, we focus on the top 25% of high-crime tracts to strike a balance between on the on hand covering most high-crime places, not only true outliers, and on the other hand not covering those tracts where variation might be noise.As the figure shows, there is not much variation in the lower quartiles

The LEHD Origin-Destination Employment Statistics

Unemployment Compensation for Federal Employees program The LODES data are published

as an annual cross-section from 2002 onwards, with each job having a workplace and residence dimension The data are available for all states but Massachusetts

A LODES place of work is defined by the physical or mailing address reported by employers in the QCEW, while workers’ residence is derived from federal administrative records For privacy purposes, LODES uses a variety of methods to shield workplace job counts and residential locations Residence coarsening occurs at most at the census tract level, which is why we use that

as our most granular level of analysis Further explanation of this process can be found in

Graham et al (2014) The extra noise is intentionally random, meaning that while it might inflate our standard errors, it should not bias our results Table 1 provides basic properties of the data at the tract-year and the origin tract-place destination-pair-year level

Data on Parolees and Released Prisoners

We use data from the Justice Atlas of Sentencing and Corrections, produced by the Justice Mapping Center, on the number of released prisoners and parolees per capita at the census tract level These data come from state-level departments of corrections, parole, and probation In

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addition, we use the home addresses of parolees in the city of Atlanta as of April 12, 2016, from the Georgia State Board of Pardons and Paroles

III High-Crime Areas and Workers with Criminal Records

There is, unfortunately, no national data on employment outcomes for individuals with criminal records, the actual treatment group In fact, the available data do not even allow for accurate tallies of the number of people with such records – estimates vary by the (tens of) millions

(Brame et al., 2012; McGinty, 2015) Our focus in this paper is instead on neighborhoods with high crime rates If workers with criminal records are more likely to live in such neighborhoods, and if Ban the Box measures work as intended, they should lead to better outcomes in these neighborhoods This reasoning relies on the fact that individuals with criminal records are more likely to live in high-crime neighborhoods

To establish this fact, we use data from the Justice Atlas of Sentencing and Corrections on

released prisoners and parolees Figure 3a and 3b plot rates of released prisoners and parolees per capita at the census tract level against the number of assaults and murders per capita from the NNCS data To ease viewing, tracts are divided into equal-population bins The figure shows that high-crime neighborhoods are home to significantly more parolees per capita and released

prisoners, and, by implication, to significantly more people with a criminal record This

relationship is evident in the figure and is highly statistically significant Going forward we will use this proxy, then, to identify tracts where people are more likely to have criminal records and

to be affected by Ban the Box legislation.1

1 Appendix Figure 1 serves as a robustness check on this finding It uses addresses-level location data on parolees published by the Georgia State Board of Pardons and Paroles We geocode these addresses, and combine them with geocoded violent crime data provided by the Atlanta Police Department at the tract level This produces a

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IV Employment Outcomes for Residents of High-Crime Areas

In this section we present our central result: that the residents of high-crime neighborhoods benefit, on average, from Ban the Box legislation We use two methods to identify the effect of such bans on the employment opportunities of these workers The first one exploits variation in crime rates across different census tracts to identify potential workers affected by bans We refer

to these estimates as between-tract The second one uses an additional layer of identifying

variation: whether the tracts in which these residents work have adopted bans or not We refer to this as within-tract variation

III.1 Cross-Tract Identification

Our first estimator is a difference-in-difference estimator that works as follows We compare employment for the residents of high-crime neighborhoods to employment for the residents of low-crime neighborhoods before and after the introduction of a ban As discussed in the previous section, to identify high-crime and low-crime census tracts, in our baseline estimates we label the 25% most violent tracts high-crime and other tracts low-crime We then estimate the following regression equation:

ln empi,t = αi + αcity × t + αhigh crime × t + β x banit x high crime i+ εit , (1)

where empi,t is the number of residents of tract i employed in period t, αi represents tract-level fixed effects, αcity*t controls for arbitrary trends at the city level with city-year pair fixed effects, and αhigh crime*t controls for arbitrary employment trends in high-crime versus low-crime tracts

We interact two dummies, for whether a tract had a ban in a certain year and whether it was a

similar pattern to that generated using Justice Atlas data Note that while property and drug crime rates are correlated with our measure, they are less reliable proxies, perhaps due to variation in reporting

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high-crime tract, to create our variable of interest We cluster standard errors at the city level (the typical treatment level), but our results are robust to clustering at the state or zip code area level and wild bootstrapping.2

The first column in Table 2 shows the results of this estimation High-crime tracts subject to a ban see employment increase by 3.5% compared to high-crime tracts in cities that were not subject to a ban, even after controlling for arbitrary high-crime tract and citywide trends.34 To test the strength of this result, we conducted a series of placebo tests In each test, we randomly re-assign our existing set of ban the box laws to placebo cities By randomly re-assigning the time series of laws as opposed to using a purely probabilistic procedure, we ensure that each placebo has the same number of cities with a ban each year as the true distribution We then re-estimate our baseline specification using the randomly assigned laws, and we repeat this

procedure 1,000 times We find that our estimate using the true assignment of laws exceeds 96.6% of the placebo estimates We therefore feel confident that the relationship we find is not a spurious one Moreover, while displacement effects are a concern, given the small fraction of employment accounted for by residents of high crime tracts, our estimates are unlikely to be driven by them.5

2 Though we have nearly 90 clusters, we also test whether our estimates are statistically significant under tests that account for small numbers of clusters In particular, we conduct a wild bootstrap estimate of our baseline specification following Cameron, Gelbach, and Miller (2008) We find that our baseline t-statistic is in the top 5.4%

of bootstrap estimates This suggests that our significance tests are not overly inflated by a small number of clusters

3 Appendix Table 2 shows that this result is not driven by concurrent population increases Appendix Table 3 uses the Coarsened Exact Matching algorithm to match areas that did and did not become subject to Ban the Box regulations as a robustness check on our baseline results

4 Similar tests show that aggregate employment is not significantly affected by the introduction of Ban the Box regulations

5 We believe that these employment gains mostly represent substitution by employers across workers rather than absolute job gains As such, our empirical estimates here pick up both employment increases in high-crime

neighborhoods and employment decreases in other neighborhoods within the same city As a result, our point estimates are not the absolute gain in high crime neighborhoods Nevertheless, since high-crime neighborhoods

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The estimate reported in column 2, which is of remarkably similar economic and statistical significance, comes from a regression that, in addition, controls for separate linear time trends in employment for low- and high-crime tracts by city Columns 3 through 6 allow for high-crime tract employment trends that vary by census division, while columns 5 and 6 show that our results barely change if we define only the 10% or 5% most violent tracts as high-crime instead

of the top 25%.6

Figure 4 shows an event study style depiction of this impact as it evolves over time, estimated using separate dummies for each pre- or post-ban year as opposed to the single post dummy included in in equation 1 above:

𝑙𝑙𝑙𝑙 𝑒𝑒𝑒𝑒𝑒𝑒 𝑖𝑖𝑖𝑖 = 𝛼𝛼 𝑖𝑖 + 𝛼𝛼 𝑐𝑐𝑖𝑖𝑖𝑖𝑐𝑐 × 𝑖𝑖 + 𝛼𝛼 ℎ𝑖𝑖𝑖𝑖ℎ 𝑐𝑐𝑐𝑐𝑖𝑖𝑐𝑐𝑐𝑐×𝑖𝑖 + 𝛽𝛽 𝑖𝑖 × ℎ𝑖𝑖𝑖𝑖ℎ 𝑐𝑐𝑐𝑐𝑖𝑖𝑒𝑒𝑒𝑒 𝑖𝑖 × 𝑦𝑦𝑒𝑒𝑦𝑦𝑐𝑐 𝑑𝑑𝑑𝑑𝑒𝑒𝑒𝑒𝑖𝑖𝑒𝑒𝑑𝑑 𝑐𝑐𝑖𝑖𝑖𝑖𝑐𝑐,𝑖𝑖 + 𝜀𝜀 𝑖𝑖𝑖𝑖 , (2)

We see no pre-trend that would lead us to believe that our estimates are somehow contaminated

by divergent trends This is reassuring, but not entirely surprising given that we control for arbitrary trends at the city level as well as between high-crime and low-crime neighborhoods What we do see is effectively a level increase in high-crime area employment in the years after the ban is introduced, with minor fluctuations around our baseline 3.5% increase estimate.7

One last concern one may have is that Ban the Box measures would be systematically correlated with other, similar legislation As far as we have been able to determine, this is not the case Not

represent a smaller fraction of neighborhoods, and even more so of overall employment, our point estimates are likely to be close to the absolute gain For example, when we restrict our sample to cities in which high-crime neighborhoods contain less than 20% of total employment, we actually estimate a slightly larger effect (a 5.8% increase in employment), and not a smaller one This suggests to us that most of the movement comes from the treated tracts as opposed to displacement from baseline declines

6 A regression analogous to the regression in column 2 but for the subsample of high-crime neighborhoods only produces an estimate of 4.1%, significant at the 10% confidence level This specification eliminates within-city cross-tract substitution, yet yields similar results

7 Appendix Figure 2 shows our results separately for high-crime and low-crime tracts, both relative to crime tracts Employment in high-crime tracts increases somewhat, while employment in low-crime tracts

medium-decreases

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only are Ban the Box measures typically standalone initiatives, they are also not correlated with perhaps the most similar type of legislation in terms of motivation and target population, bans on credit checks in application procedures Using data on such bans from Clifford and Shoag

(2016), we find no correlation between the adoption of credit check bans and Ban the Box

measures between 2007 and 2013 The correlation is insignificant for each year, and fluctuates in sign (positive for 2010, 2011, and 2012; negative for the remaining years) In addition, we find

no relationship between changes in state minimum wage laws and Ban the Box measures during the period we study This strengthens our conviction that the effects we find are not spurious or driven by unrelated concurrent public policies

III.2 Within-Tract Identification

The results in the previous subsection show quite convincingly that Ban the Box measures have a positive effect on the employment chances of the residents of high-crime areas The level of detail reported in the LODES data allows us to test the robustness of this result by exploiting not just where people reside, but also where those same people commute to work That is, we know from the data where the residents of a given tract go to work, and in some cases their commutes take these residents both to destination tracts that are subject to and destination tracts that are not subject

to Ban the Box measures In effect, what that means is that we estimate the following regression equation:

𝑙𝑙𝑙𝑙 𝑒𝑒𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜,𝑖𝑖= 𝛼𝛼𝑜𝑜𝑜𝑜+ 𝛼𝛼𝑜𝑜×𝑖𝑖+ 𝛼𝛼𝑜𝑜×𝑖𝑖+ 𝛽𝛽 × 𝑏𝑏𝑦𝑦𝑙𝑙𝑜𝑜𝑖𝑖× ℎ𝑖𝑖𝑖𝑖ℎ 𝑐𝑐𝑐𝑐𝑖𝑖𝑒𝑒𝑒𝑒𝑜𝑜+ 𝜀𝜀𝑜𝑜𝑜𝑜,𝑖𝑖 , (3)

where αod represents pair-level fixed effects that control for baseline differences across

tract-to-tract flows between origin tract o and destination tract d, αd*t controls for arbitrary trends at the destination level with destination-year fixed effects, and αo*t controls for aggregate outcomes for

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the tract in a given year These fixed effects allow us to study within-tract-year variation What

this variation allows us to learn about is the differential impact of a ban at a work location on the

employment of residents of high-crime tracts compared to the residents of a low-crime tract, conditional on all of the included fixed effects Tracts are classified as high- or low-crime tracts based on National Neighborhood Crime Study data from 1999, 2000, and 2001, well before the introduction of the bans, to ensure that crime levels are not endogenous We limit the sample to origin-destination flows with at least 10 observations

We report our estimates in Table 3 Column 1 shows that the effect is an increase in employment

of 4.1%, which is remarkably similar to our result from the previous subsection.8 Column 2 and 3 restrict the sample to observations with at least 20 and 30 commuters, respectively, which barely changes our estimates, suggesting that our results are not driven by the large numbers of origin-destination combinations with low numbers of commuters

III.3 Threats to Identification

When using a differences-in-differences-style identification strategy, one needs to be concerned about pre-existing or contemporaneous trends that might bias the estimates

For example, one might be concerned that Ban the Box policies were enacted in cities or regions with growing employment or in regions or cities where employment was growing disproportionately in high-crime neighborhoods We address this concern in numerous ways First,

we explicitly check for pre-trends in our baseline specification in Figure 4 and find none Second,

we include city-year fixed effects in Table 2, controlling for arbitrary differences in trends across

8 Appendix Figure 3 shows an event study graph similar to that in Figure 4, and again shows no significant trend

pre-16

cities This allows us to identify off differences across tracts within a city Third, we run tests that include city-specific linear trends for high-crime neighborhoods and high-crime neighborhood by census division by year fixed effects These controls enable us to identify the impact of the ban off changes for high-crime tracts relative to their own trends within the city and relative to trends for geographically close high-crime neighborhoods in other cities We find similar impacts of these bans when progressively adding all of these controls, which suggests that these types of biases did not have a large effect on our initial estimate

What threats remain after these tests? Our test would remain biased if Ban the Box laws were enacted in cities experiencing a break in the relative employment of their high-crime neighborhoods relative to prior trends for those tracts For example, suppose Boston enacted a Ban the Box law right as its high-crime neighborhoods grew over and above prior trends for those neighborhoods and trends for high-crime neighborhoods elsewhere in New England If this correlation were not confined to Boston, but was systematic across cities, it would bias our estimates Table 3 introduces a test that is robust to this possibility Rather than identify the impact off differences in total employment outcomes for a tract, it identifies off differences in commuting patterns We now explore whether residents of high-crime tracts are more likely than residents of other tracts to commute to work in BTB destinations, holding constant their overall employment outcomes Once again, we find an impact of BTB policy on these outcomes To relate this to the previous example, we now find that residents of high-crime tracts in New Hampshire have become more likely to commute to Boston, even controlling for the total number of employed people in those tracts Thus any omitted-variable bias story needs to account for both the increase in employment in high-crime tracts in Boston and the change in commuting patterns

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Now, it is impossible to rule out the potential for a complicated alternative counterfactual Still, it

is clear that straightforward bias stories about different cyclical trends or growth rates (see Appendix Table 2 for an explicit check of the latter9) cannot explain these results We believe that articulating an explanation that accounts for all of our findings in which Ban the Box policies do not have the effect claim they have is sufficiently difficult that, per Occam's razor, the best explanation is that we are indeed measuring the impact of these policies

The LODES data allow us to identify not just how many residents of given tracts are employed, but also what their wages are, that is, whether they are below $15,000 annually, between $15,000 and $40,000, or over $40,000, and in which industry category they work Note that this information

is collected at the individual level: the LODES data effectively provides counts of residents in each industry or wage category We exploit these distinctions to demonstrate what types of work and what levels of remuneration the residents of high-crime areas manage to find and receive when Ban the Box measures are implemented At this level of detail, the identification strategy of subsection III.1, which involves larger numbers of workers, is more informative than that of subsection III.2, and we revert to the former

V.1 Wage Levels

Table 4 shows our results for different wage bins The regressions we run here mimic the first column of Table 2, and allows us to estimate the increase in employment for residents of high-crime tracts subject to a ban compared to high-crime tracts in cities that were not subject to a ban,

9 Unfortunately, we do not have reliable annual population estimates by census tract We therefore run a

regression using changes between decennial population estimates in an attempt to mimic the baseline as closely

as possible

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even after controlling for tract-level fixed effects and arbitrary citywide trends for the different wage bins.10 The estimates are as one would probably expect: they are greatest for our lowest-income bin (at a little over 4%), and statistically insignificantly different from zero for annual wages over $15,000 That said, the point estimates for different income bins do not differ significantly from one another The next subsection offers a potential explanation for this result

V.2 Industries

Table 5 and 6 show our results split out by broadly defined industry.11 The regressions we estimate

in these two tables are again just like those in the first column of Table 2, this time with the sample split up by industry Table 5 shows industries that witnessed a statistically significant increase in employment for the residents of high-crime neighborhoods while Table 6 shows estimates for all other industries These latter estimates are all smaller than 4% and not different from 0 at the 95% confidence level

The industries with a large increase in high-crime area resident employment are, in order of percentage increase size, government (12.1%), information (5.3%), education (4.2%), and real estate (4.1%) Missing from this list are industries with large numbers of minimum-wage workers such as retail, accommodation, and food services, which may well explain the relatively similar effects we found for different wage bins The most obvious explanation for this is that many of the Ban the Box measures we study here apply principally to the public sector and that compliance there is likely to be higher This finding confirms Craigie’s (2020) estimates of dramatic increases

10 Note that the data form a repeated cross-section: our identification strategy relies on the assumption that, conditional on arbitrary citywide trends, the industry and wage characteristics of tract-level migrant flows are not correlated with differential changes in the industry and wage characteristics of commuting flows to nearby tracts that do and do not become subject to Ban the Box rules

11 The industry categorization is the one used in the LODES data; assignments of jobs to different categories are determined there as well Appendix Table 4 shows the crosswalk from this categorization to NAICS codes

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in public-sector employment for workers with criminal records in the NLSY In addition, most of the private-sector firms who voluntarily ceased the practice of asking about applicants’ criminal history, such as Walmart, are active in the retail industry We show our estimates for the remaining industries in Table 6, where we find particularly small point estimates in the management, waste management, and wholesale sectors

Overall, we find that the impact of BTB policies is concentrated in the industries and wage bins one would expect, which is reassuring

VI Discussion

The central finding in this paper is that Ban the Box measures improve the labor market

outcomes of residents of high-crime neighborhoods, a good proxy for the labor market outcomes

of workers with a criminal record Ban the Box legislation thus appears to have been successful

if judged on the basis of its proclaimed proximate objective: making it easier for individuals with criminal records to find and retain employment It has increased employment in the highest-crime neighborhoods by as much as 4% The mechanism through which this happened seems quite straightforward: in all likelihood, employers who used to ask about an applicant’s criminal history used to scare some potential employees away and used to choose not to interview some others In addition, the normalization of incorporating applicants’ criminal histories in the hiring process is likely to have led to a rise in the number of criminal background checks that were carried out, and Ban the Box measures appear to have stemmed this rise

Some suggestive evidence for this comes from the Survey of State Criminal History Information Systems, published by the Bureau of Justice of Statistics The survey provides us with the

number of background checks for reasons not directly related to the administration of the

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criminal justice system for 45 states in the years 2006, 2008, 2010, and 2012 We divide this number by the number of new hires in each state in the corresponding year as published by the Census Bureau in its Quarterly Workforce Indicators to create a measure of criminal background checks per hire Regressing this measure on an indicator for whether a state has implemented Ban the Box measures while controlling for year and state fixed effects shows that Ban the Box measures are associated with 0.16 fewer criminal background checks per hire, on a basis of only 0.26 background checks This decrease is significant at the 95% confidence level.12

Clifford and Shoag’s (2016) research into the effect of eliminating credit checks found that employers shifted toward the adoption of other signals to screen potential employees We do not study such upskilling responses from the demand side here, but they are likely to occur and would lead to the creation of groups of losers from the policy We leave the question whether this response has indeed materialized to future work – but if it did, Ban the Box measures must have produced groups of losers in addition to the groups of workers it benefits

Potential groups of losers from Ban the Box initiatives are the focus of Agan and Starr’s (2018), Craigie’s (2020), and Doleac and Hansen’s (forthcoming) studies, which emphasize concerns about statistical discrimination, especially against African-Americans.13 This type of

consequence, while not in direct contradiction of Ban the Box advocates’ immediate objectives, may give policymakers pause Doleac and Hansen analyze CPS data using a difference-in-difference design and focus much of their write-up on young, low-skilled black men, who in their preferred specification become 3.4% percentage points less likely to be employed after the

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introduction of Ban the Box rules Sampling variation aside, there are two obvious explanations for this effect on young, low-skilled black men The first one is that, as Doleac and Hansen argue, employers respond to Ban the Box measures by engaging in (statistical) discrimination on the basis of race, which leads to job losses among members of those racial groups most likely to have criminal records, in particular African-Americans A second, competing, explanation is that Ban the Box measures leads to a shift of labor market opportunity away from demographic groups that are less likely to have criminal records (such as young people) toward groups that are more likely to have criminal records (such as old people)

An intuitive way to distinguish between these two explanations is to look at older black men, who are more likely to have criminal records than young black men (Brame et al., 2012;

McCauley, 2017; Shoag and Veuger, 2019) Doleac and Hansen report, in their Table 7, that employment for this group increases, suggesting that statistical discrimination on the basis of race alone is not what drives the worsening outcomes for younger black men In fact, a back-of-the-envelope calculation that weights the effects reported in Doleac and Hansen’s Table 7 for various groups of black men by their population shares suggests a slight increase in employment for black men between the ages of 25 and 64 When we replicate their results, we find a similar result: a small and statistically insignificant increase in employment for black men between the ages of 25 and 64 When we use our own cross-tract specification to study employment in the 25% of tracts with the greatest share of African-Americans based on the LODES data, we again find a small and statistically insignificant results Finally, Craigie’s triple-difference estimation using NLSY data confirms that there seems to have been no large racial backlash in response to Ban the Box rules All this suggests that it is the second explanation set out above, jobs shifting from groups less likely to have criminal records to workers more likely to have criminal records,

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that accounts for the labor market consequences of Ban the Box policies If employers had instead to turned to statistical discrimination on the basis of race to proxy for criminal records, one would have expected to see job losses, not gains, among older black men as well

Policymakers may well be concerned about the distributional consequences of these policies – in that they make it so that workers less likely to have criminal records, including young workers, will face more labor market competition – but it is hard to argue that these are unintended, as opposed to logical, consequences of the policies in question